Sharpening and smoothing operations are commonly used to enhance the appearance of digital images. A sharpening operation is used to emphasize edges and features in a digital image. In contrast, a smoothing operation is used to suppress visual noise in smooth areas of a digital image. These image enhancements can be used to compensate for image degradation due to defects or irregularities in the imaging sensor, optical lens or other elements in the image capture device. Furthermore, these image enhancements can be used to improve the quality of printed images. Most inkjet printers use a halftoning process to render ink drops onto printing media to create printed images with apparent continuous tone level. The appearance of continuous tone level is achieved by modulating the ink drops in either frequency or size, according to the grayscales in the original images. However, ink drop placement error and ink drop shape variation may weaken high spatial frequency components in the original images. As a result, the printed version of original images may appear blurry.
One technique to compensate for this problem is to enhance edges (high frequency components) within an input image before the halftoning process by sharpening these edges, so that enough high frequency components remain after the halftoning process. However, if just simple conventional image sharpening process is applied to the entire input image, the noise included in the image may also get enhanced. Thus, intelligent image enhancing processes have been developed to selectively perform either a sharpening operation or a smoothing operation. These intelligent image enhancing processes first determine whether the current pixel of an input image is in a smooth area or near an edge of the image. If the current pixel is in a smooth area of the image, a smoothing operation is performed on that pixel. An example of a conventional smoothing operation involves averaging the current pixel with neighboring pixels. On the other hand, if the current pixel is near an edge of the image, a sharpening operation is performed on that pixel. An example of a conventional sharpening operation involves filtering the current pixel of an input image through a high-pass filter and then adding to the original pixel a signal proportional to the high-pass filtered version of that pixel. Thus, high-frequency components of the input image are emphasized. By determining whether a pixel of an input image is near an edge, these intelligent image enhancing processes can apply the proper operation to each pixel of an input image to enhance the image for the printing process.
A concern with the conventional intelligent image enhancing processes is that these processes tend to be computationally intensive. Thus, the conventional intelligent image sharpening processes are not well suited for ASIC hardware or printer firmware implementation.
In view of this concern, there is a need for a method and system for intelligently enhancing images in a less computationally intensive manner, and consequently, is suitable for ASIC hardware or printer firmware implementation.